# Argument Generation with Retrieval, Planning, and Realization Source: [Argument Generation with Retrieval, Planning, and Realization]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:38:30.146Z Authors: Xinyu Hua, Zhe Hu, Lu Wang Published: 2019-06-09T21:39:46.000Z ## Abstract Automatic argument generation is an appealing but challenging task. In this paper, we study the specific problem of counter-argument generation, and present a novel framework, CANDELA. It consists of a powerful retrieval system and a novel two-step generation model, where a text planning decoder first decides on the main talking points and a proper language style for each sentence, then a content realization decoder reflects the decisions and constructs an informative paragraph-level argument. Furthermore, our generation model is empowered by a retrieval system indexed with 12 million articles collected from Wikipedia and popular English news media, which provides access to high-quality content with diversity. Automatic evaluation on a large-scale dataset collected from Reddit shows that our model yields significantly higher BLEU, ROUGE, and METEOR scores than the state-of-the-art and non-trivial comparisons. Human evaluation further indicates that our system arguments are more appropriate for refutation and richer in content. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Argument Generation on ArgGen](): Argument Generation; ArgGen; BLEU-2: 14.29. - [Opinion Article Generation on OpinionGen](): Opinion Article Generation; OpinionGen; BLEU-2: 10.66. ## Original sources and implementation [Original paper]() [PDF]()